Gamification analytics matter a lot. More than ROI or KPIs.

Yu Kai Chou calls gamification human-focused design. I agree – enterprise gamification isn’t about fun (it never was) but is really about a new type of user experience or interface. Actually, the term “user experience” isn’t precise. Enterprise gamification is about the employee experience. It is about how employees experience and interface with the roles, tasks and behaviors they are expected to perform in the context of their work. It is about how they check on how they are doing and strive to improve it – a fitbit for work.

Done well, gamification provides context, calls-to-action and a sense of meaningful work to employees – but the core question remains. How can companies get to the “gamification done well” phase? Some of it lies in good gamification design, using sound principles within the context of an organization or role and the desired employee behaviors. But some of it lies in using gamification analytics properly – and this is the core of this blog post.

Analytics is a tool for doing things better

If there is one thing that the Internet – or google analytics – has taught the world is the democratization of business intelligence. Many people now look at website (or app) analytics and try to understand how people interact with their site or app. They then use this for continuous optimization of their website or app.

Gamification analytics is about that same idea: tracking behavior so that the interaction with the system (website, game or enterprise app) will become simpler, more compelling and more engaging.

Let’s use a simple example: you’ve just released a new mobile game. We’ll call it “Sharks Gone Berserk”. In this game, Sharks (all of which have gone berserk) are after you. The goal of the game is to run as fast as you can and make creative use of boxes in which you can hide. The game is split into levels which progress according to your level of mastery – just like any game. In the first levels, you are basically taught how the game works (aka onboarding). In the latter levels, you make use of the knowledge you’ve acquired to hopefully blaze through many levels and become a true master.

Now let’s say you’ve released the game to the app store, just at the same day as your very best friend released their game – “Tigers gone berserk”. After a week, your friend’s game had one thousand downloads. You have twenty.

“What could I have done better?” you tell your friend, who is willing to help you despite the obvious rivalry between your sharks and his tigers.

“Do you know how your players are doing?” he asks and adds “do you think they went through the onboarding phase and understood the game? Can they be stuck somewhere?”

“No” you respond, “I don’t track anything on the game, but I am sure they are doing well. After all, I put a lot of thought in on-boarding and in designing the game levels”.

Your friend then cracks his laptop open. “Let me show you something” he says. “I made sure I have analytics integrated into my game. I, too, had thought that the levels were even…. But see what I discovered after a week…”. What your friend discovered was that (a) people weren’t using the lesson they ought to have learned in the first onboarding phases – meaning they didn’t “get” part of the game. But the worse thing was (b). Game users went through levels 1-5 fairly easily. Almost too easily. But only 1% of them managed to get past level 6.

“What did you do?” you ask, trying to think what went wrong with your berserk sharks.

“I changed level 6” he responds.

If you want to optimize behavior, you need to optimize and analyze gamification

This is exactly the point about gamification and analytics. If gamification is supposed to drive behavior at work – and the way people make use of enterprise apps – and is also supposed to be a “fitbit for work” – how can it be used if the game designer or game manager doesn’t know how it’s used.

Imagine you include an eLearning module in a customer service gamification project. If you don’t measure it, how can you tell whether it is working? If you design a game with a dashboard showing whether an employee is “in the green” or “in the red” – shouldn’t you know if it is too easy to be in the green or almost impossible to stay out of the red?

That’s why we’ve been putting a lot of thought lately into how we use (and offer our customers to make us of) gamification analytics.

The stakeholders of gamification analytics

Mika identifies the stakeholders that should care about gamification analytics:

Game designers should track analytics to do their jobs well. They can use it to segment gamification users and target the right game rules and mechanics for them. They should be able to use gamification to see the difference between optimal game engagement and poor game engagement and more.

The gamification owner at the customer is the person who chose a gamification project to complement performance. What matters here is the ability to view and control how the gamification experience improves users’ performance in the most effective way. We can drive each employee to improve his performance in a different way, and this can be done using gamification analytics to give visibility to track performance and act accordingly to be able to boost employees’ performance.

Game administrators also use analytics – but they are more focused on tracking that everything is on track and on optimization.

Last but not least, the gamification vendor uses gamification analytics to see comparative performance across games and customers, by segment.

Here’s another great diagram by Mika, summing it all up:

Using gamification analytics for long term engagement

You began using analytics for “sharks gone berserk” to make sure people like your game, shared your game, bought your game and kept coming back to it.

The sustainability of use and the integration of gamification into work are the main point of success in gamification. A one-month “challenge” to make people work a little harder to get a badge (an example of poor game design) just isn’t going to cut it for the future of gamification.

Gamification needs optimization and a deep understanding of player segments, activities, levels and more. Only the use of analytics can ensure this really happens.